Speech Emotion Recognition Based on Decision Tree and Improved SVM Mixed Model

Juan Juan Zhao, Rui Liang Ma, Xiao Long Zhang

Research output: Contribution to journalArticle


To effectively improve the accuracy of speech emotion recognition in intelligent man-machine harmonious interaction, a method of speech emotion recognition was proposed based on decision tree and an improved SVM mixed model. This method can avoid the tree unbounded generalization error, more the number of classifiers and other shortcomings, while taking advantage of SVM-KNN mixed model to avoid constrained optimization problems and improve the recognition efficiency. In this paper, six basic emotions were identified, including sadness, joy, anger, disgust, surprise, fear. Experimental results show that this method can effectively identify six basic emotions. Compared with the traditional support vector machine and artificial neural network method, this method can get higher recognition accuracy, better stability, strong practicability and generalization ability.

Original languageEnglish (US)
Pages (from-to)386-390 and 395
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Issue number4
Publication statusPublished - Apr 1 2017


All Science Journal Classification (ASJC) codes

  • Engineering(all)

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